context("Models from previous versions of XGBoost can be loaded")

metadata <- list(
  kRounds = 2,
  kRows = 1000,
  kCols = 4,
  kForests = 2,
  kMaxDepth = 2,
  kClasses = 3
)

run_model_param_check <- function(config) {
  testthat::expect_equal(config$learner$learner_model_param$num_feature, '4')
  testthat::expect_equal(config$learner$learner_train_param$booster, 'gbtree')
}

get_num_tree <- function(booster) {
  dump <- xgb.dump(booster)
  m <- regexec('booster\\[[0-9]+\\]', dump, perl = TRUE)
  m <- regmatches(dump, m)
  num_tree <- Reduce('+', lapply(m, length))
  return(num_tree)
}

run_booster_check <- function(booster, name) {
  config <- xgb.config(booster)
  run_model_param_check(config)
  if (name == 'cls') {
    testthat::expect_equal(get_num_tree(booster),
                           metadata$kForests * metadata$kRounds * metadata$kClasses)
    testthat::expect_equal(as.numeric(config$learner$learner_model_param$base_score), 0.5)
    testthat::expect_equal(config$learner$learner_train_param$objective, 'multi:softmax')
    testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class),
                           metadata$kClasses)
  } else if (name == 'logitraw') {
    testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
    testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0)
    testthat::expect_equal(config$learner$learner_train_param$objective, 'binary:logitraw')
  } else if (name == 'logit') {
    testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
    testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0)
    testthat::expect_equal(config$learner$learner_train_param$objective, 'binary:logistic')
  } else if (name == 'ltr') {
    testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
    testthat::expect_equal(config$learner$learner_train_param$objective, 'rank:ndcg')
  } else {
    testthat::expect_equal(name, 'reg')
    testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
    testthat::expect_equal(as.numeric(config$learner$learner_model_param$base_score), 0.5)
    testthat::expect_equal(config$learner$learner_train_param$objective, 'reg:squarederror')
  }
}

test_that("Models from previous versions of XGBoost can be loaded", {
  bucket <- 'xgboost-ci-jenkins-artifacts'
  region <- 'us-west-2'
  file_name <- 'xgboost_r_model_compatibility_test.zip'
  zipfile <- tempfile(fileext = ".zip")
  extract_dir <- tempdir()
  download.file(paste('https://', bucket, '.s3-', region, '.amazonaws.com/', file_name, sep = ''),
                destfile = zipfile, mode = 'wb', quiet = TRUE)
  unzip(zipfile, exdir = extract_dir, overwrite = TRUE)
  model_dir <- file.path(extract_dir, 'models')

  pred_data <- xgb.DMatrix(matrix(c(0, 0, 0, 0), nrow = 1, ncol = 4), nthread = 2)

  lapply(list.files(model_dir), function(x) {
    model_file <- file.path(model_dir, x)
    m <- regexec("xgboost-([0-9\\.]+)\\.([a-z]+)\\.[a-z]+", model_file, perl = TRUE)
    m <- regmatches(model_file, m)[[1]]
    model_xgb_ver <- m[2]
    name <- m[3]
    is_rds <- endsWith(model_file, '.rds')
    is_json <- endsWith(model_file, '.json')
    # TODO: update this test for new RDS format
    if (is_rds) {
      return(NULL)
    }
    # Expect an R warning when a model is loaded from RDS and it was generated by version < 1.1.x
    if (is_rds && compareVersion(model_xgb_ver, '1.1.1.1') < 0) {
      booster <- readRDS(model_file)
      expect_warning(predict(booster, newdata = pred_data))
      booster <- readRDS(model_file)
      expect_warning(run_booster_check(booster, name))
    } else {
      if (is_rds) {
        booster <- readRDS(model_file)
      } else {
        booster <- xgb.load(model_file)
        xgb.parameters(booster) <- list(nthread = 2)
      }
      predict(booster, newdata = pred_data)
      run_booster_check(booster, name)
    }
  })
})
